{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Titanic\n"]},{"cell_type":"markdown","metadata":{},"source":["## Imports and Configuration\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["All libraries imported successfully.\n"]}],"source":["# Data analysis and wrangling\n","import numpy as np, pandas as pd\n","# Access to system parameters\n","import sys\n","# Suppress warnings\n","from warnings import simplefilter\n","# Visualization\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import matplotlib.pylab as pylab\n","# Default to system path\n","import os\n","os.sys.path.append(\"../\")\n","# Path\n","from pathlib import Path\n","# Project configuration\n","from Config import SETTINGS\n","# Set Matplotlib defaults\n","%matplotlib inline\n","plt.style.use([\"science\", \"ieee\", \"muted\", \"grid\"])\n","plt.rc(\"figure\", autolayout=True)\n","plt.rc(\n","    \"axes\",\n","    labelweight=\"bold\",\n","    labelsize=\"large\",\n","    titleweight=\"bold\",\n","    titlesize=14,\n","    titlepad=10,\n",")\n","sns.set_style('white')\n","pylab.rcParams['figure.figsize'] = 12, 8\n","\n","# Mute warnings\n","simplefilter('ignore')\n","\n","print('All libraries imported successfully.')\n"]},{"cell_type":"markdown","metadata":{},"source":["## Data Preparation\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.read_csv(Path(SETTINGS['ROOT_PATH']) / SETTINGS['TRAIN_PATH']) # Training data\n","test_df = pd.read_csv(Path(SETTINGS['ROOT_PATH']) / SETTINGS['TEST_PATH']) # Test data\n","\n","train_df.head()\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Pclass       891 non-null    int64  \n"," 3   Name         891 non-null    object \n"," 4   Sex          891 non-null    object \n"," 5   Age          714 non-null    float64\n"," 6   SibSp        891 non-null    int64  \n"," 7   Parch        891 non-null    int64  \n"," 8   Ticket       891 non-null    object \n"," 9   Fare         891 non-null    float64\n"," 10  Cabin        204 non-null    object \n"," 11  Embarked     889 non-null    object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["train_df.info()"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>714.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>446.000000</td>\n","      <td>0.383838</td>\n","      <td>2.308642</td>\n","      <td>29.699118</td>\n","      <td>0.523008</td>\n","      <td>0.381594</td>\n","      <td>32.204208</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>257.353842</td>\n","      <td>0.486592</td>\n","      <td>0.836071</td>\n","      <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>223.500000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>446.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>668.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>891.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       PassengerId    Survived      Pclass         Age       SibSp  \\\n","count   891.000000  891.000000  891.000000  714.000000  891.000000   \n","mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n","std     257.353842    0.486592    0.836071   14.526497    1.102743   \n","min       1.000000    0.000000    1.000000    0.420000    0.000000   \n","25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n","50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n","75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n","max     891.000000    1.000000    3.000000   80.000000    8.000000   \n","\n","            Parch        Fare  \n","count  891.000000  891.000000  \n","mean     0.381594   32.204208  \n","std      0.806057   49.693429  \n","min      0.000000    0.000000  \n","25%      0.000000    7.910400  \n","50%      0.000000   14.454200  \n","75%      0.000000   31.000000  \n","max      6.000000  512.329200  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### Setting a random state for reproducibility\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Seed for reproducibility of results has been setted to: 42\n"]}],"source":["from Utils.utils import seed_everything\n","\n","seed_everything(SETTINGS['RANDOM_STATE'])\n"]},{"cell_type":"markdown","metadata":{},"source":["## Explorative Data Analysis\n"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Data cleaning improvement suggestions. Complete them before proceeding to ML modeling.\n"]},{"data":{"text/html":["<style type=\"text/css\">\n","#T_2e05c_row0_col0, #T_2e05c_row0_col4, #T_2e05c_row1_col0, #T_2e05c_row1_col4, #T_2e05c_row4_col2, #T_2e05c_row4_col3, #T_2e05c_row11_col5 {\n","  background-color: #67000d;\n","  color: #f1f1f1;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row0_col1, #T_2e05c_row0_col6, #T_2e05c_row1_col1, #T_2e05c_row1_col6, #T_2e05c_row2_col1, #T_2e05c_row2_col6, #T_2e05c_row3_col1, #T_2e05c_row3_col6, #T_2e05c_row4_col1, #T_2e05c_row4_col6, #T_2e05c_row5_col1, #T_2e05c_row5_col6, #T_2e05c_row6_col1, #T_2e05c_row6_col6, #T_2e05c_row7_col1, #T_2e05c_row7_col6, #T_2e05c_row8_col1, #T_2e05c_row8_col6, #T_2e05c_row9_col1, #T_2e05c_row9_col6, #T_2e05c_row10_col1, #T_2e05c_row10_col6, #T_2e05c_row11_col1, #T_2e05c_row11_col6 {\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row0_col2, #T_2e05c_row0_col3, #T_2e05c_row0_col5, #T_2e05c_row1_col2, #T_2e05c_row1_col3, #T_2e05c_row1_col5, #T_2e05c_row2_col2, #T_2e05c_row2_col3, #T_2e05c_row2_col5, #T_2e05c_row3_col2, #T_2e05c_row3_col3, #T_2e05c_row3_col5, #T_2e05c_row4_col5, #T_2e05c_row5_col5, #T_2e05c_row6_col2, #T_2e05c_row6_col3, #T_2e05c_row6_col5, #T_2e05c_row7_col2, #T_2e05c_row7_col3, #T_2e05c_row7_col5, #T_2e05c_row8_col0, #T_2e05c_row8_col2, #T_2e05c_row8_col3, #T_2e05c_row8_col4, #T_2e05c_row8_col5, #T_2e05c_row9_col0, #T_2e05c_row9_col2, #T_2e05c_row9_col3, #T_2e05c_row9_col4, #T_2e05c_row10_col0, #T_2e05c_row10_col2, #T_2e05c_row10_col3, #T_2e05c_row10_col4, #T_2e05c_row10_col5, #T_2e05c_row11_col0, #T_2e05c_row11_col2, #T_2e05c_row11_col3, #T_2e05c_row11_col4 {\n","  background-color: #fff5f0;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row2_col0, #T_2e05c_row2_col4 {\n","  background-color: #c7171c;\n","  color: #f1f1f1;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row3_col0, #T_2e05c_row3_col4 {\n","  background-color: #fcb398;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row4_col0, #T_2e05c_row4_col4 {\n","  background-color: #fdd5c4;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row5_col0, #T_2e05c_row5_col4 {\n","  background-color: #fee5d9;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row5_col2, #T_2e05c_row5_col3 {\n","  background-color: #fcb99f;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row6_col0, #T_2e05c_row6_col4, #T_2e05c_row7_col0, #T_2e05c_row7_col4 {\n","  background-color: #fff4ef;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","#T_2e05c_row9_col5 {\n","  background-color: #fcbda4;\n","  color: #000000;\n","  font-family: Segoe UI;\n","}\n","</style>\n","<table id=\"T_2e05c\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_2e05c_level0_col0\" class=\"col_heading level0 col0\" >Nuniques</th>\n","      <th id=\"T_2e05c_level0_col1\" class=\"col_heading level0 col1\" >dtype</th>\n","      <th id=\"T_2e05c_level0_col2\" class=\"col_heading level0 col2\" >Nulls</th>\n","      <th id=\"T_2e05c_level0_col3\" class=\"col_heading level0 col3\" >Nullpercent</th>\n","      <th id=\"T_2e05c_level0_col4\" class=\"col_heading level0 col4\" >NuniquePercent</th>\n","      <th id=\"T_2e05c_level0_col5\" class=\"col_heading level0 col5\" >Value counts Min</th>\n","      <th id=\"T_2e05c_level0_col6\" class=\"col_heading level0 col6\" >Data cleaning improvement suggestions</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row0\" class=\"row_heading level0 row0\" >PassengerId</th>\n","      <td id=\"T_2e05c_row0_col0\" class=\"data row0 col0\" >891</td>\n","      <td id=\"T_2e05c_row0_col1\" class=\"data row0 col1\" >int64</td>\n","      <td id=\"T_2e05c_row0_col2\" class=\"data row0 col2\" >0</td>\n","      <td id=\"T_2e05c_row0_col3\" class=\"data row0 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row0_col4\" class=\"data row0 col4\" >100.000000</td>\n","      <td id=\"T_2e05c_row0_col5\" class=\"data row0 col5\" >0</td>\n","      <td id=\"T_2e05c_row0_col6\" class=\"data row0 col6\" >possible ID column: drop</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row1\" class=\"row_heading level0 row1\" >Name</th>\n","      <td id=\"T_2e05c_row1_col0\" class=\"data row1 col0\" >891</td>\n","      <td id=\"T_2e05c_row1_col1\" class=\"data row1 col1\" >object</td>\n","      <td id=\"T_2e05c_row1_col2\" class=\"data row1 col2\" >0</td>\n","      <td id=\"T_2e05c_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row1_col4\" class=\"data row1 col4\" >100.000000</td>\n","      <td id=\"T_2e05c_row1_col5\" class=\"data row1 col5\" >1</td>\n","      <td id=\"T_2e05c_row1_col6\" class=\"data row1 col6\" >combine rare categories, possible ID column: drop</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row2\" class=\"row_heading level0 row2\" >Ticket</th>\n","      <td id=\"T_2e05c_row2_col0\" class=\"data row2 col0\" >681</td>\n","      <td id=\"T_2e05c_row2_col1\" class=\"data row2 col1\" >object</td>\n","      <td id=\"T_2e05c_row2_col2\" class=\"data row2 col2\" >0</td>\n","      <td id=\"T_2e05c_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row2_col4\" class=\"data row2 col4\" >76.430976</td>\n","      <td id=\"T_2e05c_row2_col5\" class=\"data row2 col5\" >1</td>\n","      <td id=\"T_2e05c_row2_col6\" class=\"data row2 col6\" >combine rare categories</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row3\" class=\"row_heading level0 row3\" >Fare</th>\n","      <td id=\"T_2e05c_row3_col0\" class=\"data row3 col0\" >248</td>\n","      <td id=\"T_2e05c_row3_col1\" class=\"data row3 col1\" >float64</td>\n","      <td id=\"T_2e05c_row3_col2\" class=\"data row3 col2\" >0</td>\n","      <td id=\"T_2e05c_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row3_col4\" class=\"data row3 col4\" >27.833895</td>\n","      <td id=\"T_2e05c_row3_col5\" class=\"data row3 col5\" >0</td>\n","      <td id=\"T_2e05c_row3_col6\" class=\"data row3 col6\" >skewed: cap or drop outliers</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row4\" class=\"row_heading level0 row4\" >Cabin</th>\n","      <td id=\"T_2e05c_row4_col0\" class=\"data row4 col0\" >147</td>\n","      <td id=\"T_2e05c_row4_col1\" class=\"data row4 col1\" >object</td>\n","      <td id=\"T_2e05c_row4_col2\" class=\"data row4 col2\" >687</td>\n","      <td id=\"T_2e05c_row4_col3\" class=\"data row4 col3\" >77.104377</td>\n","      <td id=\"T_2e05c_row4_col4\" class=\"data row4 col4\" >16.498316</td>\n","      <td id=\"T_2e05c_row4_col5\" class=\"data row4 col5\" >1</td>\n","      <td id=\"T_2e05c_row4_col6\" class=\"data row4 col6\" >combine rare categories, fill missing, fix mixed data types</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row5\" class=\"row_heading level0 row5\" >Age</th>\n","      <td id=\"T_2e05c_row5_col0\" class=\"data row5 col0\" >88</td>\n","      <td id=\"T_2e05c_row5_col1\" class=\"data row5 col1\" >float64</td>\n","      <td id=\"T_2e05c_row5_col2\" class=\"data row5 col2\" >177</td>\n","      <td id=\"T_2e05c_row5_col3\" class=\"data row5 col3\" >19.865320</td>\n","      <td id=\"T_2e05c_row5_col4\" class=\"data row5 col4\" >9.876543</td>\n","      <td id=\"T_2e05c_row5_col5\" class=\"data row5 col5\" >0</td>\n","      <td id=\"T_2e05c_row5_col6\" class=\"data row5 col6\" >fill missing</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row6\" class=\"row_heading level0 row6\" >SibSp</th>\n","      <td id=\"T_2e05c_row6_col0\" class=\"data row6 col0\" >7</td>\n","      <td id=\"T_2e05c_row6_col1\" class=\"data row6 col1\" >int64</td>\n","      <td id=\"T_2e05c_row6_col2\" class=\"data row6 col2\" >0</td>\n","      <td id=\"T_2e05c_row6_col3\" class=\"data row6 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row6_col4\" class=\"data row6 col4\" >0.785634</td>\n","      <td id=\"T_2e05c_row6_col5\" class=\"data row6 col5\" >0</td>\n","      <td id=\"T_2e05c_row6_col6\" class=\"data row6 col6\" ></td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row7\" class=\"row_heading level0 row7\" >Parch</th>\n","      <td id=\"T_2e05c_row7_col0\" class=\"data row7 col0\" >7</td>\n","      <td id=\"T_2e05c_row7_col1\" class=\"data row7 col1\" >int64</td>\n","      <td id=\"T_2e05c_row7_col2\" class=\"data row7 col2\" >0</td>\n","      <td id=\"T_2e05c_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row7_col4\" class=\"data row7 col4\" >0.785634</td>\n","      <td id=\"T_2e05c_row7_col5\" class=\"data row7 col5\" >0</td>\n","      <td id=\"T_2e05c_row7_col6\" class=\"data row7 col6\" ></td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row8\" class=\"row_heading level0 row8\" >Pclass</th>\n","      <td id=\"T_2e05c_row8_col0\" class=\"data row8 col0\" >3</td>\n","      <td id=\"T_2e05c_row8_col1\" class=\"data row8 col1\" >int64</td>\n","      <td id=\"T_2e05c_row8_col2\" class=\"data row8 col2\" >0</td>\n","      <td id=\"T_2e05c_row8_col3\" class=\"data row8 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row8_col4\" class=\"data row8 col4\" >0.336700</td>\n","      <td id=\"T_2e05c_row8_col5\" class=\"data row8 col5\" >0</td>\n","      <td id=\"T_2e05c_row8_col6\" class=\"data row8 col6\" ></td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row9\" class=\"row_heading level0 row9\" >Embarked</th>\n","      <td id=\"T_2e05c_row9_col0\" class=\"data row9 col0\" >3</td>\n","      <td id=\"T_2e05c_row9_col1\" class=\"data row9 col1\" >object</td>\n","      <td id=\"T_2e05c_row9_col2\" class=\"data row9 col2\" >2</td>\n","      <td id=\"T_2e05c_row9_col3\" class=\"data row9 col3\" >0.224467</td>\n","      <td id=\"T_2e05c_row9_col4\" class=\"data row9 col4\" >0.336700</td>\n","      <td id=\"T_2e05c_row9_col5\" class=\"data row9 col5\" >77</td>\n","      <td id=\"T_2e05c_row9_col6\" class=\"data row9 col6\" >fill missing, fix mixed data types</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row10\" class=\"row_heading level0 row10\" >Survived</th>\n","      <td id=\"T_2e05c_row10_col0\" class=\"data row10 col0\" >2</td>\n","      <td id=\"T_2e05c_row10_col1\" class=\"data row10 col1\" >int64</td>\n","      <td id=\"T_2e05c_row10_col2\" class=\"data row10 col2\" >0</td>\n","      <td id=\"T_2e05c_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row10_col4\" class=\"data row10 col4\" >0.224467</td>\n","      <td id=\"T_2e05c_row10_col5\" class=\"data row10 col5\" >0</td>\n","      <td id=\"T_2e05c_row10_col6\" class=\"data row10 col6\" ></td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2e05c_level0_row11\" class=\"row_heading level0 row11\" >Sex</th>\n","      <td id=\"T_2e05c_row11_col0\" class=\"data row11 col0\" >2</td>\n","      <td id=\"T_2e05c_row11_col1\" class=\"data row11 col1\" >object</td>\n","      <td id=\"T_2e05c_row11_col2\" class=\"data row11 col2\" >0</td>\n","      <td id=\"T_2e05c_row11_col3\" class=\"data row11 col3\" >0.000000</td>\n","      <td id=\"T_2e05c_row11_col4\" class=\"data row11 col4\" >0.224467</td>\n","      <td id=\"T_2e05c_row11_col5\" class=\"data row11 col5\" >314</td>\n","      <td id=\"T_2e05c_row11_col6\" class=\"data row11 col6\" ></td>\n","    </tr>\n","  </tbody>\n","</table>\n"],"text/plain":["<pandas.io.formats.style.Styler at 0x2a675dd30>"]},"metadata":{},"output_type":"display_data"}],"source":["from autoviz import data_cleaning_suggestions\n","\n","data_cleaning_suggestions(train_df)"]},{"cell_type":"markdown","metadata":{},"source":["#### Dimensionality reduction with t-SNE and UMAP\n"]},{"cell_type":"markdown","metadata":{},"source":["## Applying Feature Engineering\n"]},{"cell_type":"markdown","metadata":{},"source":["### Data Cleaning\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Data cleaning for train set has been completed successfully.\n","Data cleaning for test set has been completed successfully.\n"]}],"source":["from Utils.utils import data_cleaning\n","# Drop useless columns\n","drop_list = [\n","    'PassengerId',\n","    'Name',\n","    'Ticket',\n","    'Cabin'\n","]\n","fill_list = ['Age', 'Embarked', 'Fare']\n","anomaly_list = ['Fare']\n","\n","categorical_features = ['Embarked', 'Sex']\n","numerical_features = ['Fare', 'Age', 'SibSp', 'Parch', 'Pclass']\n","\n","target_col = 'Survived'\n","\n","X, y, _, _ = data_cleaning(\n","    train_df, categorical_features, numerical_features, fill_list, drop_list, anomaly_list, target_col, mode='train')\n","X_test, _, _, _ = data_cleaning(test_df, categorical_features, numerical_features,\n","                                  fill_list, drop_list, anomaly_list, target_col, mode='test')\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Pclass</th>\n","      <th>Sex_male</th>\n","      <th>Sex_female</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Embarked_S</th>\n","      <th>Embarked_C</th>\n","      <th>Embarked_Q</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.827377</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>-0.497793</td>\n","      <td>0.432793</td>\n","      <td>-0.473674</td>\n","      <td>-0.785782</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-1.566107</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.715048</td>\n","      <td>0.432793</td>\n","      <td>-0.473674</td>\n","      <td>3.222473</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.827377</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>-0.194583</td>\n","      <td>-0.474545</td>\n","      <td>-0.473674</td>\n","      <td>-0.743530</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-1.566107</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.487640</td>\n","      <td>0.432793</td>\n","      <td>-0.473674</td>\n","      <td>2.084264</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.827377</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.487640</td>\n","      <td>-0.474545</td>\n","      <td>-0.473674</td>\n","      <td>-0.735705</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     Pclass  Sex_male  Sex_female       Age     SibSp     Parch      Fare  \\\n","0  0.827377         1           0 -0.497793  0.432793 -0.473674 -0.785782   \n","1 -1.566107         0           1  0.715048  0.432793 -0.473674  3.222473   \n","2  0.827377         0           1 -0.194583 -0.474545 -0.473674 -0.743530   \n","3 -1.566107         0           1  0.487640  0.432793 -0.473674  2.084264   \n","4  0.827377         1           0  0.487640 -0.474545 -0.473674 -0.735705   \n","\n","   Embarked_S  Embarked_C  Embarked_Q  \n","0           1           0           0  \n","1           0           1           0  \n","2           1           0           0  \n","3           1           0           0  \n","4           1           0           0  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["X.head()"]},{"cell_type":"markdown","metadata":{},"source":["### Reducing the size of your data\n"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Mem. usage decreased to  0.02 Mb (68.6% reduction)\n","Mem. usage decreased to  0.01 Mb (68.5% reduction)\n"]}],"source":["from Utils.utils import reduce_mem_usage\n","\n","X = reduce_mem_usage(X)\n","X_test = reduce_mem_usage(X_test)\n"]},{"cell_type":"markdown","metadata":{},"source":["## Modeling\n"]},{"cell_type":"markdown","metadata":{},"source":["### Establishing Baseline\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["ROC-AUC: 0.85871\n"]}],"source":["from sklearn.model_selection import cross_val_score\n","from xgboost import XGBClassifier\n","\n","baseline_model = XGBClassifier(random_state=SETTINGS['RANDOM_STATE'])\n","print(\n","    f\"ROC-AUC: {np.mean(cross_val_score(baseline_model, X, y, cv=SETTINGS['K-FOLDS'], scoring='roc_auc')):.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Feature importance\n"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>feature</th>\n","      <th>importance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Pclass</td>\n","      <td>0.202018</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Sex_male</td>\n","      <td>0.556102</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Sex_female</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Age</td>\n","      <td>0.036535</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>SibSp</td>\n","      <td>0.066484</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Parch</td>\n","      <td>0.024724</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Fare</td>\n","      <td>0.036798</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Embarked_S</td>\n","      <td>0.032835</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Embarked_C</td>\n","      <td>0.028250</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Embarked_Q</td>\n","      <td>0.016255</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      feature  importance\n","0      Pclass    0.202018\n","1    Sex_male    0.556102\n","2  Sex_female    0.000000\n","3         Age    0.036535\n","4       SibSp    0.066484\n","5       Parch    0.024724\n","6        Fare    0.036798\n","7  Embarked_S    0.032835\n","8  Embarked_C    0.028250\n","9  Embarked_Q    0.016255"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Just to get ideas to improve\n","baseline_model.fit(X, y)\n","feature_importances = pd.DataFrame({\n","    'feature': baseline_model.feature_names_in_,\n","    'importance': baseline_model.feature_importances_\n","})\n","\n","feature_importances"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"image/png":"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size 4800x3300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["order = list(feature_importances.groupby(\"feature\").mean().sort_values(\"importance\", ascending=False).index)\n","\n","%matplotlib inline\n","plt.figure()\n","sns.barplot(x=\"importance\", y=\"feature\", data=feature_importances, order=order)\n","plt.title('Baseline importance')\n","# plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Ensembling with Blending and Stacking Solutions"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Trained on 596 samples, validated on 295 samples.\n"]}],"source":["# Splitting dataset into a training and a test set\n","from sklearn.model_selection import train_test_split\n","from Config import SETTINGS\n","\n","X_train, X_val, y_train, y_val = train_test_split(\n","    X, y,   test_size=SETTINGS['TEST_SIZE'], random_state=SETTINGS['RANDOM_STATE'])\n","\n","print(f'Trained on {X_train.shape[0]} samples, validated on {X_val.shape[0]} samples.')"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":["[('SVC', SVC(probability=True, random_state=42)),\n"," ('RandomForestClassifier', RandomForestClassifier(random_state=42)),\n"," ('AdaBoostClassifier', AdaBoostClassifier(random_state=42)),\n"," ('XGBClassifier',\n","  XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n","                colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n","                early_stopping_rounds=None, enable_categorical=False,\n","                eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n","                importance_type=None, interaction_constraints='',\n","                learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n","                max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n","                missing=nan, monotone_constraints='()', n_estimators=100,\n","                n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=42,\n","                reg_alpha=0, reg_lambda=1, ...)),\n"," ('KNeighborsClassifier', KNeighborsClassifier())]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Prepare models to ensemble\n","from Utils.utils import get_base_model_list\n","from sklearn.metrics import roc_auc_score, accuracy_score\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","# Instantiate models with default hyper-parameters and Train each model on the training set\n","base_estimators = [\n","    SVC(probability=True, random_state=SETTINGS['RANDOM_STATE']).fit(\n","        X_train, y_train),\n","    RandomForestClassifier(\n","        random_state=SETTINGS['RANDOM_STATE']).fit(X_train, y_train),\n","    AdaBoostClassifier(random_state=SETTINGS['RANDOM_STATE']).fit(\n","        X_train, y_train),\n","    XGBClassifier(random_state=SETTINGS['RANDOM_STATE']).fit(X_train, y_train),\n","    KNeighborsClassifier().fit(X_train, y_train),\n","]\n","\n","base_estimators = get_base_model_list(base_estimators)\n","\n","base_estimators\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Majority voting\n","\n","For each prediction, you take the class most frequently predicted by your models."]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["import numpy as np\n","from scipy.stats import mode\n","# Predict on the test set\n","preds = np.stack([base_estimator[1].predict(X_val) for base_estimator in base_estimators]).T\n","# Ensemble all these predictions using majority voting\n","max_voting = np.apply_along_axis(mode, 1, preds)[:, 0]\n"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy for model SVC is: 0.837\n","Accuracy for model RandomForestClassifier is: 0.797\n","Accuracy for model AdaBoostClassifier is: 0.810\n","Accuracy for model XGBClassifier is: 0.797\n","Accuracy for model KNeighborsClassifier is: 0.820\n"]}],"source":["# Check the accuracy for each single model\n","for i, model in enumerate(base_estimator[0] for base_estimator in base_estimators):\n","    print(\n","        f\"Accuracy for model {model} is: {accuracy_score(y_true=y_val, y_pred=preds[:, i]):0.3f}\")\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy for Majority Voting is: 0.831\n"]}],"source":["# Check the majority voting ensemble\n","print(f\"Accuracy for Majority Voting is: {accuracy_score(y_true=y_val, y_pred=max_voting):0.3f}\")\n"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy for Voting Classifier in sklearn library is: 0.831\n"]}],"source":["# Majority/Hard voting implemented by sklearn\n","from sklearn.ensemble import VotingClassifier\n","\n","model_ensemble_voting = VotingClassifier(estimators=base_estimators, voting='hard').fit(X_train, y_train)\n","\n","print(\n","    f\"Accuracy for Voting Classifier in sklearn library is: {model_ensemble_voting.score(X_val, y_val):0.3f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Averaging of model predictions\n","\n","When averaging your predictions from different models in a competition, you can consider all your predictions as having potentially the same predictive power and use the arithmetic mean to derive an average value."]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["ROC-AUC for model SVC is: 0.87700\n","ROC-AUC for model RandomForestClassifier is: 0.87569\n","ROC-AUC for model AdaBoostClassifier is: 0.85952\n","ROC-AUC for model XGBClassifier is: 0.84662\n","ROC-AUC for model KNeighborsClassifier is: 0.87836\n"]}],"source":["proba = np.stack([base_estimator[1].predict_proba(X_val)[:, 1]\n","                 for base_estimator in base_estimators]).T\n","for i, model in enumerate(base_estimator[0] for base_estimator in base_estimators):\n","    ras = roc_auc_score(y_true=y_val, y_score=proba[:, i])\n","    print(f\"ROC-AUC for model {model} is: {ras:0.5f}\")\n"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean averaging ROC-AUC is: 0.88738\n","Geometric averaging ROC-AUC is: 0.88481\n","Harmonic averaging ROC-AUC is: 0.88367\n","Mean of powers averaging ROC-AUC is: 0.88348\n","Logarithmic averaging ROC-AUC is: 0.88724\n"]}],"source":["# Try to figure out what kind of mean works best when switching to ROC-AUC as our evaluation metric.\n","\n","print(\n","    f\"Mean averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=proba.mean(axis=1)):0.5f}\")\n","\n","print(\n","    f\"Geometric averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=proba.prod(axis=1)**(1/3)):0.5f}\")\n","\n","print(\n","    f\"Harmonic averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=1 / np.mean(1. / (proba + 1e-6), axis=1)):0.5f}\")\n","\n","print(\n","    f\"Mean of powers averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=np.mean(proba**3, axis=1)**(1/3)):0.5f}\")\n","\n","print(\n","    f\"Logarithmic averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=np.expm1(np.mean(np.log1p(proba), axis=1))):0.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Weighted averages\n","\n","We will first create a correlation matrix of our predicted probabilities, and then we proceed by:\n","\n","1. Removing the one values on the diagonal and replacing them with zeroes\n","2. Averaging the correlation matrix by row to obtain a vector\n","3.  Taking the reciprocal of each row sum\n","4.  Normalizing their sum to 1.0\n","5.  Using the resulting weighting vector in a matrix multiplication of our predicted probabilities"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Weighted averaging ROC-AUC is: 0.88771\n"]}],"source":["cormat = np.corrcoef(proba.T)\n","np.fill_diagonal(cormat, 0e1)\n","W = 1 / np.mean(cormat, axis=1)\n","W = W / sum(W) # normalizing to sum==1.0\n","weighted = proba.dot(W)\n","ras = roc_auc_score(y_true=y_val, y_score=weighted)\n","print(f\"Weighted averaging ROC-AUC is: {ras:0.5f}\")"]},{"cell_type":"markdown","metadata":{},"source":["##### Averaging in your cross-validation strategy\n","\n","You may also test at training time by running the averaging operations on the validation fold (the fold that you are not using for training your model)."]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["FOLD-0 Mean averaging ROC-AUC is: 0.89395\n","FOLD-1 Mean averaging ROC-AUC is: 0.85330\n","FOLD-2 Mean averaging ROC-AUC is: 0.89795\n","FOLD-3 Mean averaging ROC-AUC is: 0.82576\n","FOLD-4 Mean averaging ROC-AUC is: 0.85623\n","CV Mean averaging ROC-AUC is: 0.86544\n"]}],"source":["from sklearn.model_selection import KFold\n","kf = KFold(n_splits=SETTINGS['K-FOLDS'], shuffle=True,\n","           random_state=SETTINGS['RANDOM_STATE'])\n","scores = []\n","for k, (train_index, test_index) in enumerate(kf.split(X)):\n","    for model in base_estimators:\n","        model[1].fit(X.iloc[train_index, :], y[train_index])\n","\n","    proba = np.stack([base_estimator[1].predict_proba(X.iloc[test_index, :])[\n","                     :, 1] for base_estimator in base_estimators]).T\n","\n","    ras = roc_auc_score(y_true=y[test_index],\n","                        y_score=proba.mean(axis=1))\n","    scores.append(ras)\n","    print(f\"FOLD-{k} Mean averaging ROC-AUC is: {ras:0.5f}\")\n","\n","print(f\"CV Mean averaging ROC-AUC is: {np.mean(scores):0.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Correcting averaging for ROC-AUC evaluations\n","Using a min-max scaler approach, you simply convert each model’s estimates into the range 0-1 and then proceed with averaging the predictions."]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean averaging ROC-AUC is: 0.88929\n"]}],"source":["from sklearn.preprocessing import MinMaxScaler\n","\n","for model in base_estimators:\n","        model[1].fit(X_train, y_train)\n","\n","proba = np.stack([base_estimator[1].predict_proba(X_val)[:, 1]\n","                 for base_estimator in base_estimators]).T\n","\n","print(\n","    f\"Mean averaging ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=MinMaxScaler().fit_transform(proba).mean(axis=1)):0.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Stacking models together\n","\n","In stacking, you always have a meta-learner. This time, however, it is not trained on a holdout, but on the entire training set, thanks to the out-of-fold (OOF) prediction strategy."]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Stacking ROC-AUC is: 0.88995\n"]}],"source":["from sklearn.ensemble import StackingClassifier\n","from sklearn.linear_model import LogisticRegression\n","\n","model_ensemble_stacking = StackingClassifier(\n","    estimators=base_estimators, final_estimator=LogisticRegression()\n",").fit(X_train, y_train)\n","\n","print(\n","    f\"Stacking ROC-AUC is: {roc_auc_score(y_true=y_val, y_score=model_ensemble_stacking.predict_proba(X_val)[:, 1]):0.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["## Bayesian Hyperparameter Optimization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Importing core libraries\n","import joblib\n","from functools import partial\n","\n","# Classifiers\n","from xgboost import XGBClassifier\n","\n","# Model selection\n","from sklearn.model_selection import KFold\n","\n","# Metrics\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import make_scorer\n","\n","# Skopt functions\n","from skopt import BayesSearchCV\n","from skopt.callbacks import DeadlineStopper, DeltaYStopper\n","from skopt.space import Real, Categorical, Integer\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Setting the scoring function\n","scoring = make_scorer(roc_auc_score, greater_is_better=True)\n","# Setting the cross-validation strategy\n","kf = KFold(n_splits=SETTINGS['K-FOLDS'], shuffle=True,\n","           random_state=SETTINGS['RANDOM_STATE'])\n","# Setting the basic model\n","estimator_to_tune = XGBClassifier()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Setting the search space\n","search_spaces = {\n","    # Boosting learning rate\n","    'learning_rate': Real(1e-2, 1e0, 'log-uniform'),\n","    # Number of boosted trees to fit\n","    'n_estimators': Integer(10, 5000),\n","    # Maximum tree depth for base learners\n","    'max_depth': Integer(1, 50),\n","    # Maximum delta step we allow for each leaf output\n","    'max_delta_step': Integer(0, 20),\n","    # Subsample ratio of the training instance\n","    'subsample': Real(1e-1, 1e0, 'uniform'),\n","\t# Subsample ratio of columns by tree\n","    'colsample_bytree': Real(1e-2, 1e0, 'uniform'),\n","    # Subsample ratio by level in trees\n","    'colsample_bylevel': Real(1e-2, 1e0, 'uniform'),\n","    # L2 regularization\n","    'reg_lambda': Real(1e-9, 1e2, 'log-uniform'),\n","    # L1 regularization\n","    'reg_alpha': Real(1e-9, 1e2, 'log-uniform'),\n","    # Minimum loss reduction for tree partitioning\n","    'gamma': Real(1e-9, 1e2, 'log-uniform'),\n","    # Weight for the positive class\n","    'scale_pos_weight': Real(1e-6, 5e2, 'log-uniform'),\n","}\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Wrapping everything up into the Bayesian optimizer\n","opt = BayesSearchCV(estimator=estimator_to_tune,\n","                    search_spaces=search_spaces,\n","                    scoring=scoring,\n","                    cv=kf,\n","                    n_iter=60,           # max number of trials\n","                    n_jobs=-1,           # number of jobs\n","                    iid=False,\n","                    # if not iid it optimizes on the cv score\n","                    return_train_score=False,\n","                    refit=False,\n","                    # Gaussian Processes (GP)\n","                    optimizer_kwargs={'base_estimator': 'GP'},\n","                    # random state for replicability\n","                    random_state=SETTINGS['RANDOM_STATE'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from Utils.utils import report_perf\n","# Running the optimizer\n","overdone_control = DeltaYStopper(delta=1e-4)\n","# We stop if the gain of the optimization becomes too small\n","time_limit_control = DeadlineStopper(total_time=60 * 60 * 6)\n","# We impose a time limit (6 hours)\n","best_params = report_perf(opt, X, y, 'Baseline', callbacks=[overdone_control, time_limit_control])\n"]},{"cell_type":"markdown","metadata":{},"source":["## Trick"]},{"cell_type":"markdown","metadata":{},"source":["### Pseudo-labeling\n","\n","The idea is to add examples from the test set whose predictions you are confident about to your training set.\n","\n","1. Train your model\n","2. Predict on the test set\n","3. Establish a confidence measure\n","4. Select the test set elements to add\n","5. Build a new model with the combined data\n","6. Predict using this model and submit"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from Utils.pseudo_labeling import PseudoLabeling\n","from sklearn.metrics import accuracy_score\n","\n","# Pseudo labeling using fine-tuned model\n","pseudo_labeller = PseudoLabeling(\n","    model=XGBClassifier(**best_params), unlabelled_data=X_test.values, sample_rate=3e-1, random_state=SETTINGS['RANDOM_STATE'], verbose=True)\n","pseudo_labeller.fit(X_train.values, y_train.values)\n","\n","print(f\"Accuracy using Pseudo Labeling : {accuracy_score(pseudo_labeller.predict(X_val.values), y_val.values):.5f}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["### Denoising with autoencoders\n"]},{"cell_type":"markdown","metadata":{},"source":["## Post-processing\n"]},{"cell_type":"markdown","metadata":{},"source":["## Submission\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["predictions = pseudo_labeller.predict(X_test)\n","output = pd.DataFrame(\n","    {'PassengerId': test_df['PassengerId'], 'Survived': predictions})\n","\n","output.to_csv(Path(SETTINGS['ROOT_PATH']) / SETTINGS['SUBMISSION_PATH'], index=False)\n","\n","print(\"Your submission was successfully saved!\")\n"]}],"metadata":{"kernelspec":{"display_name":"haowei","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ea1877b22cf93df918ac2bc7336029b7c9d8d2ad1cd83cc5c2953e64bf67d973"}}},"nbformat":4,"nbformat_minor":2}
